Estimating canopy height and its changes at meter resolution from satellite imagery remains a challenging computer vision task with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km2across France with 1.5 m panchromatic resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-∆, a benchmark for canopy height reduction detection between images from different years at tree level—a difficult task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.

Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-sized embeddings, limiting their effectiveness and integration with other modalities like LiDAR. To close this gap, we present DUNIA, an approach to learn pixel-sized embeddings through cross-modal alignment between images and full-waveform LiDAR data. As the model is trained in a contrastive manner, the embeddings can be directly leveraged in the context of a variety of environmental monitoring tasks in a zero-shot setting. In our experiments, we demonstrate the effectiveness of the embeddings for seven such tasks: canopy height mapping, fractional canopy cover, land cover mapping, tree species identification, plant area index, crop type classification, and per-pixel waveform-based vertical structure mapping. The results show that the embeddings, along with zero-shot classifiers, often outperform specialized supervised models, even in low-data regimes. In the fine-tuning setting, we show strong performances near or better than the state-of-the-art on five out of six tasks.

Exploring vertical forest structures worldwide via remote sensing faces challenges. Recent technologies like waveform light detection and ranging (LiDAR) from NASA’s global ecosystem dynamics investigation (GEDI) and SAR tomography (TomoSAR) from future European Space Agency (ESA) BIOMASS offer promising solutions. This article assesses the performance of spaceborne GEDI and TomoSAR airborne data from an ESA’s TomoSense campaign to highlight the important role of GEDI measurements in BIOMASS algorithm training and establishing precise site-specific processing parameters. Our study in Germany’s Eifel National Park delves into the precision of GEDI and P-band TomoSAR in measuring surface [digital terrain model (DTM)] and vegetation [canopy height model (CHM)] heights. Results demonstrate that GEDI and P-band TomoSAR offer high-resolution and precise surface and vegetation heights and vertical profile measurements. While GEDI relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. The research supports improving the accuracy of both DTM and CHM utilizing GEDI beams with full-power lasers coupled with high sensitivity and signal-to-noise ratio (SNR). Ground elevation measurements are more accurate than canopy height estimates for temperate forests, with DTM RMSE about 2 m and CHM RMSE about 3 m for GEDI and TomoSAR measurements. By analyzing the vertical structure of monthly GEDI data, we note a 1-m shift in the volume peak between GEDI’s leaf-on and leaf-off periods. At the same time, TomoSAR consistently exhibits a lower volume peak by about 2 m compared to GEDI during leaf-on seasons. In conclusion, our research underscores the complementary roles of TomoSAR and GEDI in accurately mapping diverse forest types, thereby bolstering the effectiveness of the BIOMASS mission.

This paper evaluates GEDI's canopy height estimation accuracy in dense tropical forests located in Mayotte Island. It examines GEDI's ability to penetrate canopies and detect the ground, which is crucial for reliable estimates. The study tests the use of a single GEDI height metric (rh_95) in comparison with regression models using various GEDI metrics to enhance accuracy. Beam sensitivity plays a pivotal role, as it impacts significantly GEDI return waveforms and the subsequent derived height estimates. In the context of our study, GEDI tends to underestimate heights above 15 meters. Regression models outperform rh_95, mitigating the impact of beam sensitivity and canopy height (RMSE decreasing from 6.6 m to 5.5 m, bias going from -1.9 m to 0.0 m). They provide unbiased estimates, offering improved accuracies regardless of these factors. This study emphasizes GEDI's limitations and highlights regression models' potential to refine canopy height estimations in complex ecosystems where signal penetration is challenging.

This research presents a comprehensive canopy height map of French Guiana at 10 m spatial resolution, employing a data fusion approach integrating optical (Sentinel-2), radar (Sentinel-1 and ALOS), and ancillary data sources. The primary objective is to leverage a U-Net neural network model, trained and validated using Global Ecosystem Dynamics Investigation (GEDI) data as reference canopy height. We aim at understanding how canopy height prediction models can be improved through the integration of relevant remote sensing and environmental descriptors related to canopy structure. The accuracies of the generated canopy height maps are assessed against high-resolution airborne LiDAR (ALS) acquisitions conducted by the French National Forest Office. We observe that enriching input data with height above nearest drainage (HAND) as well as forest landscape information yielded improved accuracies for the prediction models. Moreover, accounting for GEDI database uncertainties, through filtering of usable waveforms and correction of geolocation errors, also resulted in a performance gain for canopy height estimation using a U-Net model.

Our study highlights the important role of GEDI measurements in BIOMASS algorithm training and the establishment of precise site-specific processing parameters. Combining GEDI measurements at sparse coordinates and SAR tomography (TomoSAR) estimates enables the creation of detailed canopy height maps (CHM). While relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. Emphasis is placed on selecting shots with over 90% sensitivity for ground return detection and GEDI beams equipped with full-power lasers. Additionally, we show the GEDI profile data’s unique capacity to investigate annual changes, revealing significant volume contributions during leaf-on periods and increased ground importance during leaf-off seasons.

This study explores the impact of Eucalyptus plantation characteristics and environmental factors on GEDI returned energy distribution. Random Forest (RF) regression was used to analyze the effect of a diverse parcel-scale Eucalyptus plantation characteristics including trees height, planting density, soil properties, understorey presence, environmental conditions and NDVI generated from Sentinel-2 as a proxy of leaf area index on the GEDI relative heights (RHn). According to the findings, as the vertical distance from the ground increases (from RH5 to RH100), the most important variable explaining a given relative height changes from "NDVI" to "Volume". Moreover, at higher quantiles of the returned energy, the behavior of the GEDI metrics becomes more dependent on a narrower set of forest and environmental characteristics. However, at low RH values, the interplay of complex canopy structures and environmental factors necessitates a combination of features to explain the observed variations.

Quantification of forest’s vertical structure in the tropics using remote sensing is a challenge. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne LiDAR data, whereas the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. We show that GEDI and P-band TomoSAR can directly measure vegetation heights and vertical profiles with high resolution and precision. The GEDI vegetation height error is 5 m at the tropical sites, similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e., RH98 from full power shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s Global Ecosystem Dynamics Investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3D images through a technique called SAR tomography (TomoSAR). The paper discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2-4 m depending on the polarization and the height of the forest layers. Compared to GEDI, TomoSAR data has a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10-40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping.

In this study, water level estimates from the Global Ecosystem Dynamics Investigation Lidar (GEDI) were validated against in situ gauge station records over Lake Geneva. The performances of the first and second releases (respectively V1 and V2) of the GEDI data products were compared. The influence of the following parameters were analyzed: (1) the signal-over-noise ratio (SNR), (2) the width of water surface peak within the waveform (gwidth), (3) the amplitude of the water surface peak within the waveform (A), (4) the viewing angle of GEDI (VA), and (5) the acquiring beam. The comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant bias and fewer deviations to in situ data than V1. In addition, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r=0.66) and 42.8 cm with V1 (r=0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA≤ 3.5°). Regarding the bias, a higher value was observed with V2 but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number.

The Global Ecosystem Dynamics Investigation (GEDI) instrument, as all FW systems, relies on very sophisticated pre-processing steps to generate a priori metrics in order to accurately estimate forest characteristics, such as forest heights and wood volume. The ever-expanding volume of acquired GEDI data, which to September 2020 comprised more than 25 billion shots, and requiring more than 90 TB of storage space, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. Therefore, to avoid metric computation, we leveraged deep learning techniques in order to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. Performance comparisons were conducted between a convolutional neural network based model that uses GEDI waveform data and a previously used, metric based, Random Forest regressor (RF). Cross-validated results showed that the CNN based model compared well against the RF counterpart for both Hdomand V. Indeed, the RMSE on the estimation of Hdomfrom the CNN based model was 1.61 m with a coefficient of determination R2of 0.90, while the RF model produced an accuracy on Hdomestimates of 1.45 m(R2=0.92). For V, CNN based estimates was 27.35 m3.ha-1(R2of 0.88), while for RF, the RMSE was 27.60 m3.ha-1 (R2=0.88).

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as low-frequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ~ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-mean-square error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

Better management of water consumption in irrigated agriculture is essential in order to save water resources. The objective of this study is to propose a new model capable of detecting the irrigation events using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) in a near real-time approach. The proposed irrigation detection model relies on the change detection in the S1 backscattering coefficients at plot scale. A tree-based approach has been constructed to detect irrigation events by studying the behavior of the S1 backscattering coefficients following irrigation events at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France) and Catalonia (northeast Spain). Auxiliary data such as the NDVI (Normalized Difference Vegetation Index) and the soil moisture estimations were integrated as additional filters to reduce ambiguities related to vegetation growth and surface roughness. The results shows that the proposed model was capable of detecting 84% of the irrigation events over Montpellier. Over Catalonia site, 90.2% of the non-irrigated plots had no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season.

Full waveform (FW) LiDAR systems have gained momentum to map forest biophysical variables in the last two decades, owing to their ability to accurately estimate canopy heights and aboveground biomass. Currently, the Global Ecosystem Dynamics Investigation (GEDI) system on board of the International Space Station (ISS) is the most recent FW spaceborne LiDAR instrument for the continuous observation of earth's forests. Here, we assess the accuracy of GEDI FW data for the estimation of stand-scale dominant heights ($H_{dom}$), and stand volume (V) using linear and nonlinear regression models based on several GEDI metrics. The models were calibrated and validated using in-situ data from Eucalyptus plantations in Brazil. Overall, the most accurate estimates of$H_{dom}$and V were obtained using the stepwise regression, with an RMSE of 1.44 m (R2 of 0.92) and 24.39 m3.ha−1(R2 of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability (R2) of$H_{dom}$and V was the metric representing the height above the ground at which 90% of the waveform energy occurs.

BACKGROUND: The current study's purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI)). Body composition and bone parameters were evaluated by dual-energy X-ray absorptiometry (DXA). RESULTS: Weight, lean mass, body mass index, femoral neck cross-sectional area (FN CSA), FN section modulus (Z), FN cross sectional moment of inertia (CSMI), intertrochanteric (IT) CSA, IT Z, IT CSMI, IT cortical thickness (CT), femoral shaft (FS) CSA, FS Z and FS CSMI were significantly greater (p < 0.05) in women with normal SMI compared to women with sarcopenia. In the whole population, SMI was positively associated with IT CSA, IT Z, IT CSMI, IT CT, FS CSA, FS Z, FS CSMI, FS CT but negatively correlated to IT buckling ratio (BR) and FS BR. CONCLUSION: The current study suggests that sarcopenia has a negative effect on hip bone strength indices in postmenopausal women.

This study aimed to assess the potential of GLAS (Geoscience Laser Altimeter System) LiDAR data to overcome the saturation at high AGB values of existing AGB map on Madagascar (Vieilledent's AGB map [1]). First, spatially distributed estimations of AGB were obtained from GLAS data. Second, the difference between the Vieilledent's AGB map and GLAS derived AGB at each GLAS footprints location was calculated and a spatially distributed additional correction factors were obtained. Thanks to the spatial structure of these additional correction factors, an ordinary kriging interpolation was thus performed to provide a continuous correction factor map. Finally, the existing and the correction factor map were summed to improve the Vieilledent's AGB map. Results showed that the integration of GLAS data overcome the saturation at high AGB of Vieilledent's AGB map and allow AGB estimation until 650 t/ha (maximum AGB values from Vieilledent AGB map was 550 t/ha).

LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution. In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.

The Geoscience Laser Altimeter System (GLAS) has provided a useful dataset for estimating forest height in many areas of the globe. Most of the studies on GLAS waveforms have focused on natural forests and only a few were conducted over forest plantations. The objective of this study was to test the best known models used for estimating canopy height and above ground biomass of intensively managed Eucalyptus plantations in Brazil using full waveform LiDAR data. Studies to estimate forest heights from LiDAR data have highlighted that the fitting coefficients of developed models are strongly dependent on environmental factors such as the region of the study site, terrain topography, and forest type. In this study, we evaluated the main models developed to predict canopy height using a combination of parameters extracted from GLAS waveforms and a digital elevation model, in order to explore which combination of parameters yields the best forest height estimates. In addition, a model to estimate above ground biomass from dominant height was calibrated.

In this study, the canopy height estimation over French Guiana was analyzed using multiple linear regressions and the Random Forest technique (RF). This analysis was based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model). Results showed that the use of statistical models based on GLAS waveforms and DEM metrics provides better canopy height estimates in comparison to that obtained by the direct method (RMSE between 3.7 and 4.9 m against 7.9 m with the direct method). The best statistical model is defined as a linear regression of waveform extent, trailing edge extent, and terrain index. Random Forest regressions showed that the waveform extent was the variable that best explained the canopy height. In addition, the estimation of GLAS canopy height by RF using only the waveform extent showed an RMSE of 4.4 m. The best configuration for canopy height estimation using RF used all the metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE=3.4 m). In our case of low relief area, the use of one or two metrics among the three used in this study in addition to the waveform extent showed a slightly lower precision on the canopy height estimation (RMSE=3.6 m). In conclusion, multiple linear regressions and RF regressions provided similar precision on the canopy height estimation.

In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. Results showed an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were also analyzed. Results indicated that forests that lose leaves during the dry season were easily discriminated from the other LTs that retain their leaves.